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Nonparametric Online Machine Learning with Kernels

Nguyen, Dang Kim Khanh 2018, Nonparametric Online Machine Learning with Kernels, Ph.D thesis, Applied Artificial Intelligence Institute, Deakin University.

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Title Nonparametric Online Machine Learning with Kernels
Author Nguyen, Dang Kim Khanh
Institution Deakin University
School Applied Artificial Intelligence Institute
Faculty Office of the Deputy Vice-Chancellor (Research)
Degree type Research doctorate
Degree name Ph.D
Thesis advisor Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Le, Trung MinhORCID iD for Le, Trung Minh orcid.org/0000-0002-7070-8093
Date submitted 2018
Summary This study researched kernel-based methods and max-margin learning for largescale datasets. It advanced several theoretical and practical aspects of kernel-based and max-margin methods at the intersection with Bayesian modelling. New learning methods were proposed to avoid the curse of kernelisation while simultaneously yielding superior accuracy compared with state-of-the-art baselines.
Language eng
Indigenous content off
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
Description of original 163 p.
Copyright notice ┬ęThe author
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30148026

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.